Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models
Mensikova, Anastasija
Mensikova, Anastasija
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Abstract
Integration of artificial intelligence (AI) into life cycle assessment (LCA) has accelerated in recent years, with numerous studies successfully adapting machine learning algorithms to support various stages of LCA. Despite this rapid development, comprehensive and broad synthesis of AI-LCA research remains limited. To this end, the contributions of this study are twofold: first, this study comprehensively reviews published work at the intersection of AI and LCA, leveraging large language models (LLMs) to identify current trends, emerging themes, and future directions. Our analyses reveal that as LCA research expands, AI adoption has grown dramatically, with a noticeable shift toward LLM approaches, continued increases in machine learning use, and statistically significant correlations between AI approaches and LCA stages. Second, this study advances hybrid literature review methods at large by integrating automated LLM-based text-mining with traditional manual techniques. This effective framework captures both high?level research trends and nuanced conceptual patterns (themes) across the field. Collectively, these findings advance LLM-assisted methodologies for large-scale, reproducible reviews across broad research domains, while also providing clear pathways towards computationally-efficient LCA in the context of rapidly developing AI technologies. In doing so, this work helps LCA practitioners incorporate state-of-the-art tools and timely insights into environmental assessments that can enhance the rigor and quality of sustainability-driven decisions and decision-making processes.
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Date
1/1/2026
Student Status
Graduate Student
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Poster
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Civil & Environmental Engineering
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College of Engineering and Mathematical Sciences
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Engineering